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Currently, active research is focused on investigating the mechanisms that regulate the development of various pathologies and their evolutionary dynamics. Epigenetic mechanisms, such as DNA methylation, play a significant role in evolutionary processes, as their changes have a faster impact on the phenotype compared to mutagenesis. In this study, we attempted to develop an algorithm for identifying differentially methylated regions associated with metabolic syndrome, which have undergone methylation changes in humans during the transition from a hunter­gatherer to a sedentary lifestyle. The application of existing whole­genome bisulfite sequencing methods is limited for ancient samples due to their low quality and fragmentation, and the approach to obtaining DNA methylation profiles differs significantly between ancient hunter­gatherer samples and modern tissues. In this study, we validated DamMet, an algorithm for reconstructing ancient methylomes. Application of DamMet to Neanderthal and Denisovan genomes showed a moderate level of correlation with previously published methylation profiles and demonstrated an underestimation of methylation levels in the reconstructed profiles by an average of 15–20 %. Additionally, we developed a new Python­based algorithm that allows for the comparison of methylomes in ancient and modern samples, despite the absence of methylation profiles in modern bone tissue within the context of obesity. This analysis involves a two­step data processing approach, where the first step involves the identification and filtration of tissue­specific methylation regions, and the second step focuses on the direct search for differentially methylated regions in specific areas associated with the researcher’s target condition. By applying this algorithm to test data, we identified 38 differentially methylated regions associated with obesity, the majority of which were located in promoter regions. The pipeline demonstrated sufficient efficiency in detecting these regions. These results confirm the feasibility of reconstructing DNA methylation profiles in ancient samples and comparing them with modern methylomes. Furthermore, possibilities for further methodological development and the implementation of a new step for studying differentially methylated positions associated with evolutionary processes are discussed.

Over millions of years of evolution, the genomes of modern insects have accumulated a significant number of mutations, which often can lead up a blind alley when carrying out phylogenetic research. Genomic differences between some representatives belonging to the same family or group are often so great that they demand using nonconventional methods of the phylogenetic analysis. It is known that molecular evolution goes by the way of not only single nucleotide substitutions, but also by larger genomic reorganizations, such as insertion or deletion of large genome fragments, and even changing the order of genes. Mitochondrial DNA genes (mtDNA) are quite often used as markers for phylogenetic research into many organisms including arthropods, because mtDNA is multicopied, is inherited maternally, does not undergo recombination and accumulates mutations quickly enough (relative to the nuclear genome). To date, a large number of full nucleotide sequences of mitogenomes (thousands of organisms) has been deposited in public databases; however, their phylogenetic analysis has obstacles, especially for representatives of the insects (Insecta), whose evolution takes a considerable part of geological time. In this work we describe the application and a comparison of two ways of the phylogenetic analysis for different groups of insects. The first method uses the variability of the nucleotide sequence of mtDNA, and the second one analyses the order of genes in full mitochondrial genomes of insects that can be used as an additional marker in phylogenetic research into representatives of the order Hymenoptera.